Attitudes Toward Immigrants In
Bibliographic record
Abstract
It has long been a part of the conventional wisdom among both social scientists and laypersons that periods of unemployment are charac? terized by higher levels of prejudice and discrimination directed at immigrant groups, particularly those of a minority ethnic or racial background. Yet surprisingly little research has addressed this issue. This article presents a study of the effects of a number of socioeconomic features of Canadian cities, particularly their un? employment rates, on the attitudes toward immigrants of their native-born residents. Using data from a national study of ethnicity and multiculturalism, we estimate several regression models predict? ing three separate dimensions of attitude toward immigrants and including as independent variables both individual characteristics and structural characteristics of city of residence. We find no evidence of a sizeable effect of local unemployment rate on attitude toward immigrants. Of the other contextual variables included in our models, the only one consistently influencing these attitudes is rate of population growth. Of the individual level variables included in the models, educational attainment and income, along with mother tongue, exhibit the strongest and most consistent effects on the attitude dimensions. A considerable body of literature has emerged over the past two decades which has suggested that economic conditions are directly related to ethnic and racial prejudice. This article studies the effects of a number of sociodemographic characteristics of Canadian cities on individual attitudes toward immigrants. Our theoretical basis is that the impact of various structural attributes of cities may produce unique social and institutional networks. These social enclosures (Breton, 1988) determine the social relations among the members of a society; specifically the way minority
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".